Correlations
df_cor <- df_survey[,c(7,81:96,110:113)]
# rename variables
df_cor <- df_cor %>%
dplyr::rename(
"Age" = age,
"Familiarity to robot" = familiarrobot,
"Familiarity to objects" = familiarobjec,
"Safety" = safety,
"Competence" = competence,
"Comfortable around" = comfortable,
"Friendliness" = friendliness,
"Creepy/Cute" = creepycute,
"Bad/Good" = badgood,
"Physical warmth" = physicalwarm,
"Social warmth" = socialwarm,
"Human-like form" = humanform,
"Human-like motion" = humanmotion,
"Social competence" = socialcompetence,
"Socialness" = socialness,
"Social intelligence" = socialintelligence,
"Intelligence" = intelligence,
"Suitability math" = suitmath,
"Suitability diff. task" = suitdifftask,
"Suitability reading" = suitread,
"Suitability art" = suitart
)
#create table
apa.cor.table(df_cor, filename="Tables/Dimensions_correlations.doc", table.number=1)
##
##
## Table 1
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3
## 1. Age 42.80 11.05
##
## 2. Familiarity to robot 23.91 29.01 -.11**
## [-.16, -.05]
##
## 3. Familiarity to objects 40.65 33.58 -.09** .61**
## [-.14, -.04] [.58, .64]
##
## 4. Safety 55.62 28.67 -.03 .27** .35**
## [-.08, .02] [.22, .32] [.31, .40]
##
## 5. Competence 39.14 28.61 -.08** .42** .32**
## [-.13, -.02] [.38, .47] [.27, .37]
##
## 6. Comfortable around 51.81 29.36 -.01 .32** .41**
## [-.07, .04] [.27, .37] [.37, .46]
##
## 7. Friendliness 48.42 29.73 -.07** .38** .44**
## [-.12, -.02] [.34, .43] [.39, .48]
##
## 8. Creepy/Cute 50.96 29.47 -.00 .29** .38**
## [-.06, .05] [.24, .34] [.33, .42]
##
## 9. Bad/Good 49.61 26.87 -.11** .41** .36**
## [-.16, -.06] [.36, .45] [.32, .41]
##
## 10. Physical warmth 38.70 29.40 -.04 .32** .39**
## [-.10, .01] [.27, .37] [.35, .44]
##
## 11. Social warmth 39.74 29.72 -.08** .41** .41**
## [-.14, -.03] [.37, .46] [.37, .45]
##
## 12. Human-like form 27.42 29.25 .01 .44** .28**
## [-.04, .07] [.40, .48] [.23, .33]
##
## 13. Human-like motion 30.10 29.26 -.01 .43** .31**
## [-.07, .04] [.39, .47] [.26, .36]
##
## 14. Social competence 34.87 27.94 -.09** .40** .34**
## [-.14, -.04] [.36, .45] [.30, .39]
##
## 15. Socialness 37.41 29.02 -.05 .42** .38**
## [-.10, .00] [.37, .46] [.33, .42]
##
## 16. Social intelligence 34.42 27.69 -.08** .43** .35**
## [-.13, -.03] [.39, .47] [.30, .39]
##
## 17. Intelligence 37.65 28.26 -.07** .42** .27**
## [-.12, -.02] [.38, .46] [.22, .32]
##
## 4 5 6 7 8 9 10
##
##
##
##
##
##
##
##
##
##
##
## .45**
## [.41, .49]
##
## .67** .49**
## [.64, .70] [.45, .53]
##
## .60** .56** .72**
## [.57, .64] [.52, .59] [.70, .75]
##
## .62** .41** .81** .73**
## [.58, .65] [.36, .45] [.79, .82] [.70, .75]
##
## .65** .67** .69** .71** .66**
## [.62, .68] [.64, .70] [.66, .72] [.68, .73] [.63, .69]
##
## .54** .50** .67** .77** .69** .60**
## [.50, .57] [.46, .53] [.64, .70] [.75, .79] [.66, .71] [.56, .63]
##
## .54** .60** .67** .81** .67** .67** .81**
## [.50, .57] [.57, .64] [.65, .70] [.79, .83] [.64, .70] [.64, .70] [.79, .83]
##
## .29** .59** .35** .49** .33** .47** .40**
## [.24, .33] [.56, .62] [.31, .40] [.45, .53] [.28, .37] [.43, .51] [.36, .44]
##
## .31** .62** .40** .54** .37** .51** .46**
## [.26, .36] [.59, .65] [.35, .44] [.50, .58] [.33, .42] [.47, .55] [.42, .50]
##
## .47** .74** .58** .72** .53** .66** .68**
## [.43, .51] [.72, .76] [.54, .61] [.70, .75] [.49, .57] [.63, .69] [.65, .70]
##
## .50** .68** .61** .77** .58** .67** .73**
## [.46, .54] [.65, .71] [.58, .64] [.75, .79] [.55, .61] [.64, .70] [.71, .75]
##
## .46** .76** .55** .70** .51** .66** .66**
## [.42, .50] [.74, .78] [.51, .58] [.67, .72] [.48, .55] [.63, .69] [.62, .68]
##
## .39** .85** .42** .52** .36** .61** .46**
## [.34, .43] [.84, .87] [.37, .46] [.48, .56] [.31, .40] [.57, .64] [.41, .50]
##
## 11 12 13 14 15 16
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .53**
## [.49, .56]
##
## .57** .88**
## [.53, .60] [.86, .89]
##
## .79** .63** .67**
## [.77, .81] [.60, .66] [.64, .70]
##
## .84** .59** .65** .86**
## [.82, .85] [.56, .63] [.62, .68] [.84, .87]
##
## .78** .65** .70** .88** .86**
## [.76, .80] [.62, .68] [.67, .72] [.87, .89] [.85, .87]
##
## .56** .65** .67** .73** .66** .78**
## [.53, .60] [.62, .68] [.64, .70] [.71, .76] [.63, .69] [.76, .80]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
##
LMERs
# set sum-to-zero contrasts
contrasts(df_survey$robot_type) <- contr.sum
contrasts(df_survey$robot_type)
## [,1]
## 1 1
## 2 -1
Bad/good
# run lmer
model_badgood <- lmer(badgood ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
# print results
summary(model_badgood)
## Linear mixed model fit by REML ['lmerMod']
## Formula: badgood ~ robot_type + (1 + robot_type | ParticipantID) + (1 |
## robot)
## Data: df_survey
##
## REML criterion at convergence: 12498.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6527 -0.4877 -0.0002 0.5230 3.9687
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 409.91 20.246
## robot_type1 34.71 5.892 -0.28
## robot (Intercept) 20.16 4.491
## Residual 259.45 16.107
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 49.663 2.298 21.612
## robot_type1 1.734 1.467 1.182
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.084
# calculate cCIs
confint(model_badgood, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 17.7419707 23.17567901
## .sig02 -0.4905815 -0.05390377
## .sig03 4.7147547 7.19896439
## .sig04 2.7434145 6.99238369
## .sigma 15.4799339 16.78058389
## (Intercept) 45.1931221 54.13322398
## robot_type1 -1.1898921 4.65660255
# model diagnostics
check_model(model_badgood)
res_model_badgood <- residuals(model_badgood)
qqPlot(res_model_badgood)
## 17 315
## 16 312
Comfortable
model_comfortable<- lmer(comfortable ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_comfortable)
## Linear mixed model fit by REML ['lmerMod']
## Formula: comfortable ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 13065.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1462 -0.5935 0.0200 0.6321 4.1633
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 375.54 19.379
## robot_type1 25.48 5.047 -0.10
## robot (Intercept) 54.15 7.359
## Residual 413.43 20.333
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 51.789 2.817 18.39
## robot_type1 -2.059 2.239 -0.92
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.013
confint(model_comfortable, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 16.9036073 22.3072049
## .sig02 -0.3798457 0.1864721
## .sig03 3.4645074 6.6310055
## .sig04 4.5878947 11.0938361
## .sigma 19.5402478 21.1825134
## (Intercept) 46.3295831 57.2484193
## robot_type1 -6.4883427 2.3694607
# model diagnostics
check_model(model_comfortable)
res_model_comfortable <- residuals(model_comfortable)
qqPlot(res_model_comfortable)
## 1433 1195
## 1424 1188
Competence
model_competence<- lmer(competence ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_competence)
## Linear mixed model fit by REML ['lmerMod']
## Formula: competence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12556.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2673 -0.5243 -0.0389 0.5632 3.2183
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 380.26 19.500
## robot_type1 65.35 8.084 0.05
## robot (Intercept) 49.48 7.034
## Residual 259.56 16.111
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 39.099 2.734 14.299
## robot_type1 8.560 2.203 3.886
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.010
confint(model_competence, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 17.0947027 22.3570523
## .sig02 -0.1666206 0.2527308
## .sig03 6.8062893 9.5747924
## .sig04 4.4699116 10.6047092
## .sigma 15.4830389 16.7835287
## (Intercept) 33.7892069 44.4087930
## robot_type1 4.2083386 12.9118990
# model diagnostics
check_model(model_competence)
res_model_competence <- residuals(model_competence)
qqPlot(res_model_competence)
## 51 2
## 50 1
Creepy/Cute
model_creepycute<- lmer(creepycute ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_creepycute)
## Linear mixed model fit by REML ['lmerMod']
## Formula: creepycute ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 13264.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2066 -0.6245 0.0021 0.6606 3.6145
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 282.54 16.809
## robot_type1 18.60 4.313 -0.44
## robot (Intercept) 67.51 8.216
## Residual 502.14 22.409
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 50.986 2.887 17.660
## robot_type1 -3.231 2.476 -1.305
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.037
confint(model_creepycute, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 14.5460860 19.48412266
## .sig02 -0.8418928 -0.09885304
## .sig03 2.2240717 6.09776117
## .sig04 5.1011414 12.28761140
## .sigma 21.5357211 23.34420865
## (Intercept) 45.3925030 56.58040160
## robot_type1 -8.1034057 1.64251360
# model diagnostics
check_model(model_creepycute)
res_model_creepycute <- residuals(model_creepycute)
qqPlot(res_model_creepycute)
## 212 1194
## 210 1187
Familiarity objects
model_familiarobj<- lmer(familiarobjec ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_familiarobj)
## Linear mixed model fit by REML ['lmerMod']
## Formula: familiarobjec ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 13341.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2023 -0.6073 -0.0501 0.5620 3.5112
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 531.08 23.045
## robot_type1 37.33 6.110 0.05
## robot (Intercept) 87.73 9.366
## Residual 489.82 22.132
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 40.7404 3.4761 11.720
## robot_type1 0.1626 2.8223 0.058
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.006
confint(model_familiarobj, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 20.145787 26.4913523
## .sig02 -0.215264 0.3162658
## .sig03 4.449626 7.8429488
## .sig04 5.906282 14.0283231
## .sigma 21.268678 23.0566533
## (Intercept) 34.004460 47.4766022
## robot_type1 -5.411589 5.7367428
# model diagnostics
check_model(model_familiarobj)
res_model_familiarobj <- residuals(model_familiarobj)
qqPlot(res_model_familiarobj)
## 466 1338
## 463 1329
Familiarity robots
model_familiarrobo<- lmer(familiarrobot ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_familiarrobo)
## Linear mixed model fit by REML ['lmerMod']
## Formula: familiarrobot ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12961.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6592 -0.5612 -0.0880 0.3602 4.2934
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 379.00 19.468
## robot_type1 39.19 6.260 0.05
## robot (Intercept) 52.23 7.227
## Residual 372.54 19.301
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 24.167 2.788 8.667
## robot_type1 4.463 2.223 2.008
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.008
confint(model_familiarrobo, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 16.98365514 22.4081905
## .sig02 -0.20149171 0.2956393
## .sig03 4.86568492 7.7820548
## .sig04 4.52763225 10.9033872
## .sigma 18.54875159 20.1077151
## (Intercept) 18.76006426 29.5756474
## robot_type1 0.06750653 8.8574180
# model diagnostics
check_model(model_familiarrobo)
res_model_familiarrobo <- residuals(model_familiarrobo)
qqPlot(res_model_familiarrobo) # slight deviation
## 1440 826
## 1431 821
# robust lmer
model_familiarrobo_robust <- rlmer(familiarrobot ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_familiarrobo_robust)
## Robust linear mixed model fit by DAStau
## Formula: familiarrobot ~ robot_type + (1 + robot_type | ParticipantID) + (1 | robot)
## Data: df_survey
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3577 -0.5257 -0.0499 0.4935 6.7033
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 337.38 18.368
## robot_type1 50.95 7.138 0.24
## robot (Intercept) 33.42 5.781
## Residual 193.47 13.909
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 20.472 2.442 8.382
## robot_type1 4.798 1.874 2.561
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.058
##
## Robustness weights for the residuals:
## 1126 weights are ~= 1. The remaining 305 ones are summarized as
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.201 0.479 0.651 0.660 0.901 0.998
##
## Robustness weights for the random effects:
## 224 weights are ~= 1. The remaining 28 ones are summarized as
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.195 0.367 0.588 0.572 0.790 0.884
##
## Rho functions used for fitting:
## Residuals:
## eff: smoothed Huber (k = 1.345, s = 10)
## sig: smoothed Huber, Proposal 2 (k = 1.345, s = 10)
## Random Effects, variance component 1 (ParticipantID):
## eff: smoothed Huber (k = 5.14, s = 10)
## vcp: smoothed Huber (k = 5.14, s = 10)
## Random Effects, variance component 2 (robot):
## eff: smoothed Huber (k = 1.345, s = 10)
## vcp: smoothed Huber, Proposal 2 (k = 1.345, s = 10)
# calculate Wald confidence interval, since assumptins are violated, https://gist.github.com/kamermanpr/aaa598485b6e990017375359ff5f4533
confint.rlmerMod <- function(object, level = 0.95) {
# Extract beta coefficients
beta <- fixef(object)
# Extract names of coefficients
parm <- names(beta)
# Extract standard errors for the coefficients
se <- sqrt(diag(vcov(object)))
# Set level of confidence interval
z <- qnorm((1 + level) / 2)
# Calculate CI
ctab <- cbind(beta - (z * se),
beta + (z * se))
# label column names
colnames(ctab) <- c(paste(100 * ((1 - level) / 2), '%'),
paste(100 * ((1 + level) / 2), '%'))
# Output
return(ctab[parm, ])
}
confint.rlmerMod(model_familiarrobo_robust)
## 2.5 % 97.5 %
## (Intercept) 15.685195 25.25929
## robot_type1 1.125698 8.46971
check_model(model_familiarrobo_robust)
## Failed to compute posterior predictive checks with `re_formula=NULL`.
## Trying again with `re_formula=NA` now.
Friendliness
model_friendliness<- lmer(friendliness ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_friendliness)
## Linear mixed model fit by REML ['lmerMod']
## Formula: friendliness ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12909.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9366 -0.5765 0.0519 0.6305 3.7754
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 389.31 19.731
## robot_type1 21.03 4.586 -0.15
## robot (Intercept) 119.99 10.954
## Residual 364.76 19.099
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 48.459 3.675 13.188
## robot_type1 -3.205 3.230 -0.992
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.010
confint(model_friendliness, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 17.2567443 22.7001680
## .sig02 -0.4307711 0.1408623
## .sig03 3.0916041 6.0675611
## .sig04 6.9744268 16.1809028
## .sigma 18.3541941 19.8955036
## (Intercept) 41.3379499 55.5789793
## robot_type1 -9.5492336 3.1398194
# model diagnostics
check_model(model_friendliness)
res_model_friendliness <- residuals(model_friendliness)
qqPlot(res_model_friendliness)
## 212 572
## 210 569
Human-like motion
model_humanmotion<- lmer(humanmotion ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_humanmotion)
## Linear mixed model fit by REML ['lmerMod']
## Formula: humanmotion ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12725.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0855 -0.5919 -0.0631 0.5364 3.4856
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 259.01 16.094
## robot_type1 26.98 5.194 -0.01
## robot (Intercept) 219.41 14.813
## Residual 321.31 17.925
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 30.038 4.546 6.607
## robot_type1 8.209 4.328 1.897
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.000
confint(model_humanmotion, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 14.0319190 18.5840981
## .sig02 -0.2672357 0.2572067
## .sig03 3.8845383 6.5913556
## .sig04 9.4694747 21.6496210
## .sigma 17.2262477 18.6728079
## (Intercept) 21.2128780 38.8623934
## robot_type1 -0.2417754 16.6601034
# model diagnostics
check_model(model_humanmotion)
res_model_humanmotion <- residuals(model_humanmotion)
qqPlot(res_model_humanmotion)
## 834 1171
## 829 1164
Intelligence
model_intelligence<- lmer(intelligence ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_intelligence)
## Linear mixed model fit by REML ['lmerMod']
## Formula: intelligence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12546.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1766 -0.5420 -0.0387 0.5456 3.1854
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 350.25 18.715
## robot_type1 58.67 7.659 0.07
## robot (Intercept) 55.18 7.428
## Residual 261.32 16.166
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 37.572 2.775 13.538
## robot_type1 9.176 2.296 3.997
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.012
confint(model_intelligence, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 16.3956902 21.475240
## .sig02 -0.1504999 0.275638
## .sig03 6.4071824 9.114086
## .sig04 4.7229858 11.144351
## .sigma 15.5353797 16.840410
## (Intercept) 32.1851444 42.959327
## robot_type1 4.6461884 13.706154
# model diagnostics
check_model(model_intelligence)
res_model_intelligence <- residuals(model_intelligence)
qqPlot(res_model_intelligence)
## 451 51
## 448 50
Physical warmth
model_physicalwarm<- lmer(physicalwarm ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_physicalwarm)
## Linear mixed model fit by REML ['lmerMod']
## Formula: physicalwarm ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12753.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3884 -0.6244 -0.0490 0.5626 3.3612
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 381.3 19.53
## robot_type1 36.0 6.00 -0.26
## robot (Intercept) 100.1 10.00
## Residual 315.9 17.77
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 38.729 3.427 11.302
## robot_type1 -7.064 2.977 -2.373
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.025
confint(model_physicalwarm, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 17.0981279 22.43719205
## .sig02 -0.4808024 -0.02032746
## .sig03 4.7099143 7.42684839
## .sig04 6.3779974 14.81953000
## .sigma 17.0814651 18.51685831
## (Intercept) 32.0863380 45.37180322
## robot_type1 -12.9182953 -1.20979522
# model diagnostics
check_model(model_physicalwarm)
res_model_physicalwarm <- residuals(model_physicalwarm)
qqPlot(res_model_physicalwarm)
## 722 724
## 717 719
Safety
model_safety<- lmer(safety ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_safety)
## Linear mixed model fit by REML ['lmerMod']
## Formula: safety ~ robot_type + (1 + robot_type | ParticipantID) + (1 |
## robot)
## Data: df_survey
##
## REML criterion at convergence: 12719
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3081 -0.5137 0.0407 0.5593 4.3948
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 471.04 21.703
## robot_type1 26.74 5.171 -0.32
## robot (Intercept) 12.89 3.591
## Residual 311.22 17.641
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 55.700 2.285 24.378
## robot_type1 -2.266 1.231 -1.841
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.105
confint(model_safety, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 19.0035576 24.83891916
## .sig02 -0.5431764 -0.06122011
## .sig03 3.8718302 6.53142892
## .sig04 2.0386444 5.78063756
## .sigma 16.9548175 18.37891964
## (Intercept) 51.2444665 60.15545951
## robot_type1 -4.7291893 0.19706748
# model diagnostics
check_model(model_safety)
res_model_safety <- residuals(model_safety)
qqPlot(res_model_safety)
## 21 731
## 20 726
Social competence
model_socialcompetence<- lmer(socialcompetence ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_socialcompetence)
## Linear mixed model fit by REML ['lmerMod']
## Formula: socialcompetence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12637
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6458 -0.6183 -0.0222 0.5555 4.2547
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 385.94 19.645
## robot_type1 18.24 4.271 -0.04
## robot (Intercept) 90.39 9.507
## Residual 296.13 17.208
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 34.785 3.310 10.508
## robot_type1 2.721 2.809 0.969
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.003
confint(model_socialcompetence, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 17.2127910 22.557942
## .sig02 -0.3154945 0.245470
## .sig03 2.9431267 5.606762
## .sig04 6.0580665 14.095964
## .sigma 16.5373709 17.926527
## (Intercept) 28.3713233 41.197939
## robot_type1 -2.8120514 8.254445
# model diagnostics
check_model(model_socialcompetence)
res_model_socialcompetence <- residuals(model_socialcompetence)
qqPlot(res_model_socialcompetence)
## 1172 451
## 1165 448
Social intelligence
model_socialintelligence<- lmer(socialintelligence ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_socialintelligence)
## Linear mixed model fit by REML ['lmerMod']
## Formula: socialintelligence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12523.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5439 -0.6248 0.0013 0.5593 3.3147
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 391.89 19.796
## robot_type1 27.31 5.226 0.01
## robot (Intercept) 84.06 9.168
## Residual 265.12 16.283
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 34.352 3.234 10.622
## robot_type1 3.792 2.724 1.392
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.001
confint(model_socialintelligence, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 17.3629474 22.7090182
## .sig02 -0.2387262 0.2501142
## .sig03 4.0490872 6.5124651
## .sig04 5.8533862 13.6112617
## .sigma 15.6478649 16.9622398
## (Intercept) 28.0850076 40.6189222
## robot_type1 -1.5758420 9.1593222
# model diagnostics
check_model(model_socialintelligence)
res_model_socialintelligence <- residuals(model_socialintelligence)
qqPlot(res_model_socialintelligence)
## 171 451
## 169 448
Socialness
model_socialness<- lmer(socialness ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_socialness)
## Linear mixed model fit by REML ['lmerMod']
## Formula: socialness ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12755.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8639 -0.6319 -0.0289 0.5779 4.4368
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 410.25 20.255
## robot_type1 21.79 4.668 -0.31
## robot (Intercept) 106.81 10.335
## Residual 322.25 17.951
## Number of obs: 1431, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 37.4337 3.5423 10.567
## robot_type1 0.7259 3.0510 0.238
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.023
confint(model_socialness, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 17.7446196 23.26311609
## .sig02 -0.5615289 -0.03888504
## .sig03 3.3111821 6.05735892
## .sig04 6.5902617 15.29568438
## .sigma 17.2516974 18.70046512
## (Intercept) 30.5701628 44.29704044
## robot_type1 -5.2769402 6.72870636
# model diagnostics
check_model(model_socialness)
res_model_social <- residuals(model_socialness)
qqPlot(res_model_social)
## 212 931
## 210 925
Social warmth
model_socialwarm<- lmer(socialwarm ~ robot_type + (1 + robot_type | ParticipantID) + (1|robot), data = df_survey)
summary(model_socialwarm)
## Linear mixed model fit by REML ['lmerMod']
## Formula: socialwarm ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## REML criterion at convergence: 12881.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5499 -0.6378 -0.0109 0.6306 3.4603
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 401.45 20.036
## robot_type1 28.19 5.309 -0.27
## robot (Intercept) 121.44 11.020
## Residual 349.78 18.702
## Number of obs: 1432, groups: ParticipantID, 120; robot, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 39.778 3.703 10.742
## robot_type1 -2.064 3.256 -0.634
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.020
confint(model_socialwarm, level = 0.95, method = 'profile')
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 17.537292 23.035352227
## .sig02 -0.506202 -0.003058121
## .sig03 3.931684 6.764499257
## .sig04 7.026514 16.281315422
## .sigma 17.973572 19.482695663
## (Intercept) 32.600204 46.954609807
## robot_type1 -8.460365 4.332820515
# model diagnostics
check_model(model_socialwarm)
res_model_socialwarm <- residuals(model_socialwarm)
qqPlot(res_model_socialwarm)
## 931 1194
## 925 1187
CLMMs
Bad/good
model_clmm_badgood <- clmm(rankbadgood ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_badgood)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: rankbadgood ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1392 -3359.55 6751.10 2504(7512) 1.99e-03 9.5e+02
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.4481 0.6694
## robot_type2 1.7527 1.3239 -1.000
## robot (Intercept) 0.3722 0.6101
## Number of groups: ParticipantID 116, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 -0.2451 0.1925 -1.274 0.203
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -2.737446 0.204793 -13.367
## 2|3 -1.876557 0.193108 -9.718
## 3|4 -1.298412 0.188858 -6.875
## 4|5 -0.826980 0.186752 -4.428
## 5|6 -0.404775 0.185700 -2.180
## 6|7 -0.001065 0.185370 -0.006
## 7|8 0.403493 0.185676 2.173
## 8|9 0.825262 0.186690 4.420
## 9|10 1.292708 0.188725 6.850
## 10|11 1.862574 0.192815 9.660
## 11|12 2.719460 0.204394 13.305
## (48 observations deleted due to missingness)
# profile CI
confint(model_clmm_badgood, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.13883233 -2.33605937
## 2|3 -2.25504269 -1.49807170
## 3|4 -1.66856588 -0.92825736
## 4|5 -1.19300782 -0.46095207
## 5|6 -0.76873994 -0.04081056
## 6|7 -0.36438305 0.36225221
## 7|8 0.03957443 0.76741068
## 8|9 0.45935631 1.19116739
## 9|10 0.92281384 1.66260266
## 10|11 1.48466358 2.24048494
## 11|12 2.31885557 3.12006391
## robot_type1 -0.62235947 0.13207525
Comfortable
model_clmm_comfortable <- clmm(rankcomfortable ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_comfortable)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: rankcomfortable ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1404 -3386.61 6805.23 2936(8808) 3.42e-03 1.2e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.3469 0.5890
## robot_type2 1.3875 1.1779 -1.000
## robot (Intercept) 0.4806 0.6932
## Number of groups: ParticipantID 117, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 0.2013 0.2127 0.947 0.344
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -2.72482 0.22554 -12.082
## 2|3 -1.85449 0.21486 -8.631
## 3|4 -1.26971 0.21105 -6.016
## 4|5 -0.80050 0.20927 -3.825
## 5|6 -0.38574 0.20843 -1.851
## 6|7 0.01185 0.20821 0.057
## 7|8 0.41107 0.20853 1.971
## 8|9 0.83084 0.20948 3.966
## 9|10 1.29769 0.21132 6.141
## 10|11 1.86836 0.21504 8.689
## 11|12 2.72986 0.22563 12.099
## (36 observations deleted due to missingness)
# profile CI
confint(model_clmm_comfortable, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.166859878 -2.28277618
## 2|3 -2.275618663 -1.43336449
## 3|4 -1.683361302 -0.85605791
## 4|5 -1.210648394 -0.39034346
## 5|6 -0.794262092 0.02277311
## 6|7 -0.396230077 0.41993275
## 7|8 0.002352552 0.81977754
## 8|9 0.420278946 1.24140876
## 9|10 0.883510684 1.71186751
## 10|11 1.446895653 2.28983000
## 11|12 2.287633987 3.17207766
## robot_type1 -0.215508394 0.61808853
Competence
model_clmm_competence <- clmm(rankcompetence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_competence)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: rankcompetence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1380 -3080.40 6192.80 2366(7099) 3.38e-03 1.4e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.2778 0.5271
## robot_type2 1.0806 1.0395 -1.000
## robot (Intercept) 0.8639 0.9295
## Number of groups: ParticipantID 115, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 -1.2233 0.2785 -4.393 0.0000112 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -3.4640 0.2966 -11.678
## 2|3 -2.4122 0.2857 -8.444
## 3|4 -1.6247 0.2805 -5.793
## 4|5 -0.9616 0.2777 -3.462
## 5|6 -0.3815 0.2765 -1.380
## 6|7 0.1464 0.2761 0.530
## 7|8 0.6473 0.2764 2.342
## 8|9 1.1408 0.2773 4.114
## 9|10 1.6670 0.2789 5.977
## 10|11 2.2962 0.2822 8.138
## 11|12 3.2211 0.2913 11.056
## (60 observations deleted due to missingness)
# profile CI
confint(model_clmm_competence, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -4.0453556 -2.8826051
## 2|3 -2.9720920 -1.8522739
## 3|4 -2.1743491 -1.0749818
## 4|5 -1.5059570 -0.4172424
## 5|6 -0.9233225 0.1603760
## 6|7 -0.3946961 0.6875896
## 7|8 0.1056124 1.1890868
## 8|9 0.5973252 1.6842008
## 9|10 1.1203373 2.2136209
## 10|11 1.7431655 2.8492054
## 11|12 2.6501309 3.7921617
## robot_type1 -1.7690344 -0.6774831
Creepy/Cute
model_clmm_creepycute <- clmm(rankcreepycute ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_creepycute)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: rankcreepycute ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1404 -3340.31 6712.62 2171(6514) 2.26e-03 1.6e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.2381 0.4880
## robot_type2 1.0448 1.0222 -1.000
## robot (Intercept) 0.6664 0.8164
## Number of groups: ParticipantID 117, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 0.4248 0.2451 1.733 0.083 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -2.81878 0.25880 -10.892
## 2|3 -1.92118 0.24902 -7.715
## 3|4 -1.31370 0.24552 -5.351
## 4|5 -0.82162 0.24385 -3.369
## 5|6 -0.38767 0.24305 -1.595
## 6|7 0.02351 0.24281 0.097
## 7|8 0.43455 0.24306 1.788
## 8|9 0.87246 0.24389 3.577
## 9|10 1.35770 0.24557 5.529
## 10|11 1.94249 0.24893 7.803
## 11|12 2.81699 0.25840 10.902
## (36 observations deleted due to missingness)
# profile CI
confint(model_clmm_creepycute, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.32602833 -2.31153959
## 2|3 -2.40924829 -1.43310832
## 3|4 -1.79490712 -0.83248758
## 4|5 -1.29954908 -0.34368199
## 5|6 -0.86404608 0.08869776
## 6|7 -0.45239552 0.49940949
## 7|8 -0.04183220 0.91093921
## 8|9 0.39445054 1.35047757
## 9|10 0.87638513 1.83900512
## 10|11 1.45459240 2.43038504
## 11|12 2.31052902 3.32345383
## robot_type1 -0.05554627 0.90514246
Friendliness
model_clmm_friendliness <- clmm(rankfriendliness ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_friendliness)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: rankfriendliness ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1392 -3270.64 6573.28 2544(10107) 1.43e-03 2.4e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.3784 0.6152
## robot_type2 1.4004 1.1834 -1.000
## robot (Intercept) 1.0840 1.0412
## Number of groups: ParticipantID 116, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 0.2105 0.3092 0.681 0.496
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -2.91078 0.31966 -9.106
## 2|3 -1.99268 0.31157 -6.396
## 3|4 -1.37629 0.30867 -4.459
## 4|5 -0.88065 0.30730 -2.866
## 5|6 -0.44008 0.30665 -1.435
## 6|7 -0.01818 0.30647 -0.059
## 7|8 0.40625 0.30668 1.325
## 8|9 0.85823 0.30739 2.792
## 9|10 1.36953 0.30884 4.434
## 10|11 2.00849 0.31193 6.439
## 11|12 2.95643 0.32049 9.225
## (48 observations deleted due to missingness)
# profile CI
confint(model_clmm_friendliness, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.5373132 -2.2842512
## 2|3 -2.6033420 -1.3820264
## 3|4 -1.9812700 -0.7713103
## 4|5 -1.4829419 -0.2783534
## 5|6 -1.0411107 0.1609548
## 6|7 -0.6188457 0.5824842
## 7|8 -0.1948384 1.0073361
## 8|9 0.2557643 1.4606915
## 9|10 0.7642179 1.9748374
## 10|11 1.3971164 2.6198611
## 11|12 2.3282860 3.5845789
## robot_type1 -0.3956088 0.8165198
Intelligence
model_clmm_intelligence <- clmm(rankintelligence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_intelligence)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: rankintelligence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1380 -3045.90 6123.79 2308(9215) 4.00e-03 1.7e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.1632 0.4040
## robot_type2 0.6864 0.8285 -1.000
## robot (Intercept) 1.0696 1.0342
## Number of groups: ParticipantID 115, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 -1.2987 0.3065 -4.237 0.0000227 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -3.5879 0.3261 -11.004
## 2|3 -2.4886 0.3153 -7.892
## 3|4 -1.6463 0.3100 -5.310
## 4|5 -0.9507 0.3074 -3.093
## 5|6 -0.3478 0.3061 -1.136
## 6|7 0.1903 0.3057 0.622
## 7|8 0.6895 0.3060 2.253
## 8|9 1.1794 0.3068 3.844
## 9|10 1.7064 0.3083 5.536
## 10|11 2.3472 0.3113 7.539
## 11|12 3.2938 0.3201 10.289
## (60 observations deleted due to missingness)
# profile CI
confint(model_clmm_intelligence, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -4.22695880 -2.9488149
## 2|3 -3.10661393 -1.8704901
## 3|4 -2.25398257 -1.0386174
## 4|5 -1.55318821 -0.3482907
## 5|6 -0.94770958 0.2521954
## 6|7 -0.40894617 0.7895335
## 7|8 0.08971773 1.2892042
## 8|9 0.57808159 1.7806269
## 9|10 1.10224967 2.3106322
## 10|11 1.73701591 2.9574790
## 11|12 2.66635938 3.9211657
## robot_type1 -1.89953521 -0.6979263
Physical warmth
model_clmm_physicalwarm <- clmm(rankphysicalwarmth ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_physicalwarm)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: rankphysicalwarmth ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1404 -3237.64 6507.28 2444(9589) 3.80e-03 1.8e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.4352 0.6597
## robot_type2 1.7105 1.3079 -1.000
## robot (Intercept) 0.9565 0.9780
## Number of groups: ParticipantID 117, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 0.7435 0.2931 2.537 0.0112 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -3.10173 0.30433 -10.192
## 2|3 -2.13370 0.29510 -7.230
## 3|4 -1.47136 0.29170 -5.044
## 4|5 -0.92535 0.29002 -3.191
## 5|6 -0.43821 0.28919 -1.515
## 6|7 0.02071 0.28894 0.072
## 7|8 0.47962 0.28919 1.658
## 8|9 0.95340 0.28998 3.288
## 9|10 1.46968 0.29151 5.042
## 10|11 2.10627 0.29469 7.147
## 11|12 3.07145 0.30388 10.107
## (36 observations deleted due to missingness)
# profile CI
confint(model_clmm_physicalwarm, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.6982061 -2.5052553
## 2|3 -2.7120807 -1.5553205
## 3|4 -2.0430755 -0.8996402
## 4|5 -1.4937861 -0.3569164
## 5|6 -1.0050169 0.1285983
## 6|7 -0.5456009 0.5870127
## 7|8 -0.0871856 1.0464217
## 8|9 0.3850548 1.5217463
## 9|10 0.8983254 2.0410287
## 10|11 1.5286886 2.6838598
## 11|12 2.4758494 3.6670455
## robot_type1 0.1691235 1.3179116
Safety
model_clmm_safety <- clmm(ranksafety ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_safety)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: ranksafety ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1404 -3379.92 6791.83 2600(7800) 8.39e-03 6.7e+02
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.6917 0.8317
## robot_type2 2.6984 1.6427 -1.000
## robot (Intercept) 0.2620 0.5119
## Number of groups: ParticipantID 117, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 0.2727 0.1727 1.578 0.114
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -2.77572 0.18067 -15.364
## 2|3 -1.91718 0.16757 -11.441
## 3|4 -1.33511 0.16276 -8.203
## 4|5 -0.86063 0.16039 -5.366
## 5|6 -0.43246 0.15920 -2.717
## 6|7 -0.01977 0.15884 -0.124
## 7|8 0.39287 0.15925 2.467
## 8|9 0.82325 0.16049 5.130
## 9|10 1.30480 0.16294 8.008
## 10|11 1.89920 0.16796 11.308
## 11|12 2.79467 0.18206 15.350
## (36 observations deleted due to missingness)
# profile CI
confint(model_clmm_safety, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.12982417 -2.4216181
## 2|3 -2.24561685 -1.5887405
## 3|4 -1.65411789 -1.0161012
## 4|5 -1.17499052 -0.5462637
## 5|6 -0.74448670 -0.1204431
## 6|7 -0.33109711 0.2915511
## 7|8 0.08074780 0.7049941
## 8|9 0.50870586 1.1377955
## 9|10 0.98543956 1.6241531
## 10|11 1.57001427 2.2283921
## 11|12 2.43783471 3.1514956
## robot_type1 -0.06590834 0.6112097
Social competence
model_clmm_socialcomp <- clmm(ranksocialcompetence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_socialcomp)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula:
## ranksocialcompetence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1404 -3272.93 6577.86 2434(9530) 1.45e-03 2.2e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.3103 0.5571
## robot_type2 1.1479 1.0714 -1.000
## robot (Intercept) 1.0613 1.0302
## Number of groups: ParticipantID 117, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 -0.4711 0.3054 -1.542 0.123
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -3.07393 0.31876 -9.644
## 2|3 -2.10321 0.30989 -6.787
## 3|4 -1.41794 0.30633 -4.629
## 4|5 -0.85132 0.30454 -2.795
## 5|6 -0.35496 0.30370 -1.169
## 6|7 0.09624 0.30348 0.317
## 7|8 0.53011 0.30368 1.746
## 8|9 0.97044 0.30431 3.189
## 9|10 1.44954 0.30554 4.744
## 10|11 2.03481 0.30809 6.605
## 11|12 2.90417 0.31546 9.206
## (36 observations deleted due to missingness)
# profile CI
confint(model_clmm_socialcomp, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.69867546 -2.4491750
## 2|3 -2.71058957 -1.4958240
## 3|4 -2.01832478 -0.8175481
## 4|5 -1.44820082 -0.2544430
## 5|6 -0.95020979 0.2402839
## 6|7 -0.49855943 0.6910448
## 7|8 -0.06510177 1.1253202
## 8|9 0.37399707 1.5668852
## 9|10 0.85068805 2.0483979
## 10|11 1.43096073 2.6386692
## 11|12 2.28586900 3.5224634
## robot_type1 -1.06970680 0.1275498
Social intelligence
model_clmm_socialint <- clmm(ranksocialintelligence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_socialint)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula:
## ranksocialintelligence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1392 -3244.48 6520.97 2284(8980) 4.41e-03 1.9e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.3044 0.5517
## robot_type2 1.1493 1.0721 -1.000
## robot (Intercept) 0.8853 0.9409
## Number of groups: ParticipantID 116, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 -0.6151 0.2806 -2.192 0.0284 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -3.06604 0.29520 -10.386
## 2|3 -2.08170 0.28525 -7.298
## 3|4 -1.39901 0.28135 -4.973
## 4|5 -0.84602 0.27943 -3.028
## 5|6 -0.36164 0.27851 -1.298
## 6|7 0.08334 0.27822 0.300
## 7|8 0.51211 0.27843 1.839
## 8|9 0.95639 0.27914 3.426
## 9|10 1.44947 0.28060 5.166
## 10|11 2.04387 0.28355 7.208
## 11|12 2.91256 0.29172 9.984
## (48 observations deleted due to missingness)
# profile CI
confint(model_clmm_socialint, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.64462491 -2.48744567
## 2|3 -2.64077657 -1.52262101
## 3|4 -1.95043490 -0.84758275
## 4|5 -1.39370267 -0.29833771
## 5|6 -0.90751731 0.18423928
## 6|7 -0.46197421 0.62864705
## 7|8 -0.03359393 1.05781971
## 8|9 0.40927961 1.50350038
## 9|10 0.89949928 1.99943982
## 10|11 1.48812615 2.59960624
## 11|12 2.34080682 3.48430971
## robot_type1 -1.16496885 -0.06519609
Socialness
model_clmm_socialness <- clmm(ranksocial ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_socialness)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: ranksocial ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1404 -3326.77 6685.54 2599(7798) 4.06e-03 2.0e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.2943 0.5425
## robot_type2 1.1130 1.0550 -1.000
## robot (Intercept) 0.8827 0.9395
## Number of groups: ParticipantID 117, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 -0.2116 0.2796 -0.757 0.449
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -2.87384 0.29197 -9.843
## 2|3 -1.96443 0.28333 -6.933
## 3|4 -1.33824 0.28007 -4.778
## 4|5 -0.82618 0.27847 -2.967
## 5|6 -0.37381 0.27773 -1.346
## 6|7 0.04812 0.27753 0.173
## 7|8 0.46242 0.27776 1.665
## 8|9 0.89487 0.27847 3.214
## 9|10 1.37873 0.27990 4.926
## 10|11 1.96838 0.28281 6.960
## 11|12 2.83755 0.29095 9.753
## (36 observations deleted due to missingness)
# profile CI
confint(model_clmm_socialness, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.44609387 -2.3015860
## 2|3 -2.51974603 -1.4091089
## 3|4 -1.88715997 -0.7893142
## 4|5 -1.37197843 -0.2803823
## 5|6 -0.91815310 0.1705314
## 6|7 -0.49582617 0.5920628
## 7|8 -0.08198297 1.0068183
## 8|9 0.34908058 1.4406662
## 9|10 0.83013400 1.9273325
## 10|11 1.41408564 2.5226729
## 11|12 2.26730307 3.4077904
## robot_type1 -0.75960893 0.3364457
Social warmth
model_clmm_socialwarm <- clmm(ranksocialwarmth ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey)
summary(model_clmm_socialwarm)
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: ranksocialwarmth ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## data: df_survey
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1392 -3288.35 6608.70 2593(10183) 2.80e-03 1.9e+03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.4527 0.6728
## robot_type2 1.7531 1.3240 -1.000
## robot (Intercept) 0.8524 0.9232
## Number of groups: ParticipantID 116, robot 12
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## robot_type1 0.2815 0.2776 1.014 0.311
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -2.900019 0.287840 -10.075
## 2|3 -1.984856 0.278799 -7.119
## 3|4 -1.367529 0.275577 -4.962
## 4|5 -0.867582 0.274038 -3.166
## 5|6 -0.422393 0.273301 -1.546
## 6|7 0.001141 0.273080 0.004
## 7|8 0.423151 0.273315 1.548
## 8|9 0.865580 0.274065 3.158
## 9|10 1.360621 0.275607 4.937
## 10|11 1.982598 0.278912 7.108
## 11|12 2.912189 0.288120 10.108
## (48 observations deleted due to missingness)
# profile CI
confint(model_clmm_socialwarm, level=0.95, type=profile)
## 2.5 % 97.5 %
## 1|2 -3.4641745 -2.3358631
## 2|3 -2.5312918 -1.4384207
## 3|4 -1.9076502 -0.8274087
## 4|5 -1.4046868 -0.3304769
## 5|6 -0.9580531 0.1132676
## 6|7 -0.5340865 0.5363686
## 7|8 -0.1125362 0.9588375
## 8|9 0.3284231 1.4027378
## 9|10 0.8204405 1.9008019
## 10|11 1.4359407 2.5292544
## 11|12 2.3474835 3.4768938
## robot_type1 -0.2626151 0.8256794
GLMERs
# create new rank variable with 1 for being ranked 1st and 0 for not being ranked first
df_survey <- df_survey %>%
mutate(rank_glmer_goodbad = dplyr::case_when(
rankbadgood == "1" ~ "1",
rankbadgood != "1" ~ "0",
),
rank_glmer_comfortable = dplyr::case_when(
rankcomfortable == "1" ~ "1",
rankcomfortable != "1" ~ "0",
),
rank_glmer_competence = dplyr::case_when(
rankcompetence == "1" ~ "1",
rankcompetence != "1" ~ "0",),
rank_glmer_creepycute = dplyr::case_when(
rankcreepycute == "1" ~ "1",
rankcreepycute != "1" ~ "0",
),
rank_glmer_friendliness = dplyr::case_when(
rankfriendliness == "1" ~ "1",
rankfriendliness != "1" ~ "0",
),
rank_glmer_humanform = dplyr::case_when(
rankhumanform == "1" ~ "1",
rankhumanform != "1" ~ "0",
),
rank_glmer_intelligence = dplyr::case_when(
rankintelligence == "1" ~ "1",
rankintelligence != "1" ~ "0",
),
rank_glmer_physicalwarm = dplyr::case_when(
rankphysicalwarmth == "1" ~ "1",
rankphysicalwarmth != "1" ~ "0",
),
rank_glmer_safety = dplyr::case_when(
ranksafety == "1" ~ "1",
ranksafety != "1" ~ "0",
),
rank_glmer_socialcompetence = dplyr::case_when(
ranksocialcompetence == "1" ~ "1",
ranksocialcompetence != "1" ~ "0",
),
rank_glmer_socialintelligence = dplyr::case_when(
ranksocialintelligence == "1" ~ "1",
ranksocialintelligence != "1" ~ "0",
),
rank_glmer_social= dplyr::case_when(
ranksocial == "1" ~ "1",
ranksocial != "1" ~ "0",
),
rank_glmer_socialwarmth = dplyr::case_when(
ranksocialwarmth == "1" ~ "1",
ranksocialwarmth != "1" ~ "0",
)
)
# turn into numeric
df_survey <- df_survey %>%
mutate(across(c(starts_with("rank_glmer")),as.numeric
))
Bad/Good
model_glmer_badgood1 <- glmer(rank_glmer_goodbad ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# singularity warning, so use maximal complex random intercepts instead
model_glmer_badgood2 <- glmer(rank_glmer_goodbad ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_badgood3 <- glmer(rank_glmer_goodbad ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, use allFit command
model_allfit1 <- allFit(model_glmer_badgood1) # all issue warnings
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0170783 (tol = 0.002, component 1)
## [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
### save summary in new object
summ_allfit1 <- summary(model_allfit1)
### look at fixed estimates
summ_allfit1$fixef # differences relatively small
## (Intercept) robot_type1
## bobyqa -2.822995 0.4707894
## Nelder_Mead -2.822993 0.4707962
## nlminbwrap -2.823001 0.4708028
## nmkbw -2.823092 0.4707453
## optimx.L-BFGS-B -2.822963 0.4707861
## nloptwrap.NLOPT_LN_NELDERMEAD -2.823079 0.4708202
## nloptwrap.NLOPT_LN_BOBYQA -2.688313 0.4172696
### look at random effect
summ_allfit1$sdcor # differences small
## ParticipantID.(Intercept)
## bobyqa 0.10003491
## Nelder_Mead 0.10003719
## nlminbwrap 0.10003679
## nmkbw 0.10005726
## optimx.L-BFGS-B 0.10003284
## nloptwrap.NLOPT_LN_NELDERMEAD 0.10004642
## nloptwrap.NLOPT_LN_BOBYQA 0.09281313
## ParticipantID.robot_type1.(Intercept)
## bobyqa 0.4563101
## Nelder_Mead 0.4563094
## nlminbwrap 0.4563093
## nmkbw 0.4563169
## optimx.L-BFGS-B 0.4563064
## nloptwrap.NLOPT_LN_NELDERMEAD 0.4563719
## nloptwrap.NLOPT_LN_BOBYQA 0.4352824
## ParticipantID.robot_type1 robot.(Intercept)
## bobyqa -1.0000000 0.8724615
## Nelder_Mead -1.0000000 0.8724702
## nlminbwrap -1.0000000 0.8724426
## nmkbw -0.9999267 0.8723933
## optimx.L-BFGS-B -1.0000000 0.8724627
## nloptwrap.NLOPT_LN_NELDERMEAD -1.0000000 0.8724666
## nloptwrap.NLOPT_LN_BOBYQA -1.0000000 0.8797207
### check log likelihood
summ_allfit1$llik
## bobyqa Nelder_Mead
## -376.1508 -376.1508
## nlminbwrap nmkbw
## -376.1508 -376.1508
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## -376.1508 -376.1508
## nloptwrap.NLOPT_LN_BOBYQA
## -376.2717
### check whether all model ran
summ_allfit1$which.OK # all ran
## bobyqa Nelder_Mead
## TRUE TRUE
## nlminbwrap nmkbw
## TRUE TRUE
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## TRUE TRUE
## nloptwrap.NLOPT_LN_BOBYQA
## TRUE
# use bobyqa
model_glmer_badgood_bobyqa <- glmer(rank_glmer_goodbad ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"), control = glmerControl(optimizer="bobyqa")) # no singularity warning anymore
## boundary (singular) fit: see help('isSingular')
summary(model_glmer_badgood_bobyqa) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: rank_glmer_goodbad ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 764.3 795.7 -376.2 752.3 1386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.5273 -0.3461 -0.2541 -0.1386 6.6827
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.01001 0.1000
## robot_type1 0.20822 0.4563 -1.00
## robot (Intercept) 0.76119 0.8725
## Number of obs: 1392, groups: ParticipantID, 116; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.8230 0.2949 -9.574 <0.0000000000000002 ***
## robot_type1 0.4708 0.2896 1.625 0.104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.131
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
# wald CI
confint(model_glmer_badgood_bobyqa,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -3.40089070 -2.245099
## robot_type1 -0.09690684 1.038486
Competence
model_glmer_competence1 <- glmer(rank_glmer_competence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# singularity warning, so use maximal complex random intercepts instead
model_glmer_competence2 <- glmer(rank_glmer_competence ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_competence3 <- glmer(rank_glmer_competence ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# allfit command
model_allfit_comp1 <- allFit(model_glmer_competence1)
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0235343 (tol = 0.002, component 1)
## [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00345934 (tol = 0.002, component 1)
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
### save summary in new object
summ_allfit_comp1 <- summary(model_allfit_comp1)
### look at fixed estimates
summ_allfit_comp1$fixef # differences relatively small
## (Intercept) robot_type1
## bobyqa -3.693365 1.2467985
## Nelder_Mead -3.699265 1.2528709
## nlminbwrap -3.693371 1.2467779
## nmkbw -3.692845 1.2464801
## optimx.L-BFGS-B -3.693269 1.2466730
## nloptwrap.NLOPT_LN_NELDERMEAD -3.693360 1.2468739
## nloptwrap.NLOPT_LN_BOBYQA -3.291942 0.9163101
### look at random effect
summ_allfit_comp1$sdcor # differences small
## ParticipantID.(Intercept)
## bobyqa 0.4515016
## Nelder_Mead 0.4564540
## nlminbwrap 0.4514970
## nmkbw 0.4512882
## optimx.L-BFGS-B 0.4514487
## nloptwrap.NLOPT_LN_NELDERMEAD 0.4514898
## nloptwrap.NLOPT_LN_BOBYQA 0.2857117
## ParticipantID.robot_type1.(Intercept)
## bobyqa 0.6126591
## Nelder_Mead 0.6188293
## nlminbwrap 0.6126546
## nmkbw 0.6123600
## optimx.L-BFGS-B 0.6125946
## nloptwrap.NLOPT_LN_NELDERMEAD 0.6126369
## nloptwrap.NLOPT_LN_BOBYQA 0.4016278
## ParticipantID.robot_type1 robot.(Intercept)
## bobyqa -1.0000000 1.284159
## Nelder_Mead -1.0000000 1.283160
## nlminbwrap -1.0000000 1.284154
## nmkbw -0.9999920 1.284280
## optimx.L-BFGS-B -1.0000000 1.284243
## nloptwrap.NLOPT_LN_NELDERMEAD -0.9999998 1.284164
## nloptwrap.NLOPT_LN_BOBYQA -1.0000000 1.329337
### check log likelihood
summ_allfit_comp1$llik # differences small
## bobyqa Nelder_Mead
## -308.1529 -308.1507
## nlminbwrap nmkbw
## -308.1529 -308.1531
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## -308.1529 -308.1529
## nloptwrap.NLOPT_LN_BOBYQA
## -308.5237
### check whether all model ran
summ_allfit_comp1$which.OK # all ran
## bobyqa Nelder_Mead
## TRUE TRUE
## nlminbwrap nmkbw
## TRUE TRUE
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## TRUE TRUE
## nloptwrap.NLOPT_LN_BOBYQA
## TRUE
# -> continue with singularity warning
summary(model_glmer_competence1) # significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## rank_glmer_competence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 628.3 659.7 -308.2 616.3 1374
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7083 -0.2027 -0.1319 -0.0828 6.9313
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.2039 0.4515
## robot_type1 0.3753 0.6127 -1.00
## robot (Intercept) 1.6491 1.2842
## Number of obs: 1380, groups: ParticipantID, 115; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.6934 0.5339 -6.917 0.0000000000046 ***
## robot_type1 1.2468 0.5316 2.345 0.019 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.428
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
# wald CI
confint(model_glmer_competence1,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -4.739832 -2.646876
## robot_type1 0.204793 2.288808
Comfortable
model_glmer_comfortable1 <- glmer(rank_glmer_comfortable ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
# singularity warning, so use maximal complex random intercepts instead
model_glmer_comfortable2 <- glmer(rank_glmer_comfortable ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_comfortable3 <- glmer(rank_glmer_comfortable ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# allfit command
model_allfit_comf1 <- allFit(model_glmer_comfortable1)
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00224001 (tol = 0.002, component 1)
## [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
### save summary in new object
summ_allfit_comf1 <- summary(model_allfit_comf1)
### look at fixed estimates
summ_allfit_comf1$fixef # differences relatively small
## (Intercept) robot_type1
## bobyqa -2.713701 -0.1087037
## Nelder_Mead -2.731062 -0.1324399
## nlminbwrap -2.713700 -0.1087003
## nmkbw -2.731016 -0.1323768
## optimx.L-BFGS-B -2.731050 -0.1324333
## nloptwrap.NLOPT_LN_NELDERMEAD -2.713669 -0.1086758
## nloptwrap.NLOPT_LN_BOBYQA -2.713698 -0.1087301
### look at random effect
summ_allfit_comf1$sdcor # differences small
## ParticipantID.(Intercept)
## bobyqa 0.00000000
## Nelder_Mead 0.06620380
## nlminbwrap 0.00000000
## nmkbw 0.06620030
## optimx.L-BFGS-B 0.06620815
## nloptwrap.NLOPT_LN_NELDERMEAD 0.00000000
## nloptwrap.NLOPT_LN_BOBYQA 0.00000000
## ParticipantID.robot_type1.(Intercept)
## bobyqa 0.3874325
## Nelder_Mead 0.4331076
## nlminbwrap 0.3874366
## nmkbw 0.4331382
## optimx.L-BFGS-B 0.4331086
## nloptwrap.NLOPT_LN_NELDERMEAD 0.3874068
## nloptwrap.NLOPT_LN_BOBYQA 0.3874366
## ParticipantID.robot_type1 robot.(Intercept)
## bobyqa NaN 0.7756755
## Nelder_Mead 1.0000000 0.7739354
## nlminbwrap NaN 0.7756741
## nmkbw 0.9999971 0.7740192
## optimx.L-BFGS-B 1.0000000 0.7739375
## nloptwrap.NLOPT_LN_NELDERMEAD NaN 0.7756903
## nloptwrap.NLOPT_LN_BOBYQA NaN 0.7756689
### check log likelihood
summ_allfit_comf1$llik # differences small
## bobyqa Nelder_Mead
## -379.1287 -378.9350
## nlminbwrap nmkbw
## -379.1287 -378.9350
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## -378.9350 -379.1287
## nloptwrap.NLOPT_LN_BOBYQA
## -379.1287
### check whether all model ran
summ_allfit_comf1$which.OK # all ran
## bobyqa Nelder_Mead
## TRUE TRUE
## nlminbwrap nmkbw
## TRUE TRUE
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## TRUE TRUE
## nloptwrap.NLOPT_LN_BOBYQA
## TRUE
# -> continue with singularity warning
summary(model_glmer_comfortable1) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## rank_glmer_comfortable ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 769.9 801.3 -378.9 757.9 1398
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6125 -0.3273 -0.2244 -0.1914 5.4204
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.004507 0.06713
## robot_type1 0.191272 0.43735 1.00
## robot (Intercept) 0.600138 0.77469
## Number of obs: 1404, groups: ParticipantID, 117; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.7348 0.2678 -10.210 <0.0000000000000002 ***
## robot_type1 -0.1339 0.2597 -0.515 0.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.031
# wald CI
confint(model_glmer_comfortable1,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -3.2597818 -2.2098420
## robot_type1 -0.6428533 0.3751402
Friendliness
# rank_glmer_friendliness
model_glmer_friendliness <- glmer(rank_glmer_friendliness ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
summary(model_glmer_friendliness)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## rank_glmer_friendliness ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 696.9 728.4 -342.5 684.9 1386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8015 -0.3050 -0.2021 -0.1033 7.7668
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.003669 0.06057
## robot_type1 0.307761 0.55476 1.00
## robot (Intercept) 1.940861 1.39315
## Number of obs: 1392, groups: ParticipantID, 116; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.2059 0.4444 -7.214 0.000000000000543 ***
## robot_type1 -0.1340 0.4351 -0.308 0.758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.021
# wald CI
confint(model_glmer_friendliness,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -4.0768585 -2.3348612
## robot_type1 -0.9868149 0.7187896
Human-like form
# rank_glmer_humanform
model_glmer_humanform1 <- glmer(rank_glmer_humanform ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0682576 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
# convergence warning, so use maximal complex random intercepts instead
model_glmer_humanform2 <- glmer(rank_glmer_humanform ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit")) # singularity warning
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_humanform3 <- glmer(rank_glmer_humanform ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# allfit command
model_allfit_humanform1 <- allFit(model_glmer_humanform1)
## bobyqa :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.056521 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
### save summary in new object
summ_allfit_humanform1 <- summary(model_allfit_humanform1)
### look at fixed estimates
summ_allfit_humanform1$fixef # differences large
## (Intercept) robot_type1
## bobyqa -6.565954 3.769859
## Nelder_Mead -6.630736 3.849182
## nlminbwrap -6.598697 3.818572
## nmkbw -4.149879 1.406099
## optimx.L-BFGS-B -6.568154 3.790405
## nloptwrap.NLOPT_LN_NELDERMEAD -6.630795 3.849288
## nloptwrap.NLOPT_LN_BOBYQA -3.871897 1.244048
### look at random effect
summ_allfit_humanform1$sdcor # differences large
## ParticipantID.(Intercept)
## bobyqa 3.0866121771
## Nelder_Mead 3.2576848077
## nlminbwrap 3.1860689140
## nmkbw 0.0001280365
## optimx.L-BFGS-B 3.1213201357
## nloptwrap.NLOPT_LN_NELDERMEAD 3.2578246675
## nloptwrap.NLOPT_LN_BOBYQA 0.3664043944
## ParticipantID.robot_type1.(Intercept)
## bobyqa 3.1753684212
## Nelder_Mead 3.3439285830
## nlminbwrap 3.2731623941
## nmkbw 0.0001664081
## optimx.L-BFGS-B 3.2089439826
## nloptwrap.NLOPT_LN_NELDERMEAD 3.3439381364
## nloptwrap.NLOPT_LN_BOBYQA 0.4246317277
## ParticipantID.robot_type1 robot.(Intercept)
## bobyqa -1.0000000 1.725465
## Nelder_Mead -1.0000000 1.729363
## nlminbwrap -1.0000000 1.724014
## nmkbw -0.4062133 1.601747
## optimx.L-BFGS-B -1.0000000 1.714896
## nloptwrap.NLOPT_LN_NELDERMEAD -1.0000000 1.729265
## nloptwrap.NLOPT_LN_BOBYQA -1.0000000 1.595963
### check log likelihood
summ_allfit_humanform1$llik # differences large
## bobyqa Nelder_Mead
## -244.8129 -244.8087
## nlminbwrap nmkbw
## -244.8088 -249.0529
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## -244.8117 -244.8087
## nloptwrap.NLOPT_LN_BOBYQA
## -249.1687
### check whether all model ran
summ_allfit_humanform1$which.OK # all ran
## bobyqa Nelder_Mead
## TRUE TRUE
## nlminbwrap nmkbw
## TRUE TRUE
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## TRUE TRUE
## nloptwrap.NLOPT_LN_BOBYQA
## TRUE
# -> Redo with reduced model
# allfit command
model_allfit_humanform2 <- allFit(model_glmer_humanform2)
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw : [failed]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
### save summary in new object
summ_allfit_humanform2 <- summary(model_allfit_humanform2)
### look at fixed estimates
summ_allfit_humanform2$fixef # small differences
## (Intercept) robot_type1
## bobyqa -4.149729 1.406134
## Nelder_Mead -4.149716 1.406166
## nlminbwrap -4.149744 1.406151
## optimx.L-BFGS-B -4.149758 1.406107
## nloptwrap.NLOPT_LN_NELDERMEAD -4.149627 1.406148
## nloptwrap.NLOPT_LN_BOBYQA -4.149714 1.406126
### look at random effect
summ_allfit_humanform2$sdcor # small differences
## ParticipantID:robot_type.(Intercept)
## bobyqa 0
## Nelder_Mead 0
## nlminbwrap 0
## optimx.L-BFGS-B 0
## nloptwrap.NLOPT_LN_NELDERMEAD 0
## nloptwrap.NLOPT_LN_BOBYQA 0
## ParticipantID.(Intercept) robot.(Intercept)
## bobyqa 0 1.601706
## Nelder_Mead 0 1.601717
## nlminbwrap 0 1.601725
## optimx.L-BFGS-B 0 1.601716
## nloptwrap.NLOPT_LN_NELDERMEAD 0 1.601670
## nloptwrap.NLOPT_LN_BOBYQA 0 1.601724
### check log likelihood
summ_allfit_humanform2$llik # small differences
## bobyqa Nelder_Mead
## -249.0529 -249.0529
## nlminbwrap optimx.L-BFGS-B
## -249.0529 -249.0529
## nloptwrap.NLOPT_LN_NELDERMEAD nloptwrap.NLOPT_LN_BOBYQA
## -249.0529 -249.0529
### check whether all model ran
summ_allfit_humanform2$which.OK #
## bobyqa Nelder_Mead
## TRUE TRUE
## nlminbwrap nmkbw
## TRUE FALSE
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## TRUE TRUE
## nloptwrap.NLOPT_LN_BOBYQA
## TRUE
# -> continue with singularity warning of reduced model
summary(model_glmer_humanform2) # significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: rank_glmer_humanform ~ robot_type + (1 | ParticipantID) + (1 |
## ParticipantID:robot_type) + (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 508.1 534.3 -249.1 498.1 1399
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0073 -0.1706 -0.0855 -0.0730 12.1811
##
## Random effects:
## Groups Name Variance Std.Dev.
## ParticipantID:robot_type (Intercept) 0.0000000000000 0.0000000
## ParticipantID (Intercept) 0.0000000001041 0.0000102
## robot (Intercept) 2.5654881354194 1.6017141
## Number of obs: 1404, groups:
## ParticipantID:robot_type, 234; ParticipantID, 117; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.1497 0.5718 -7.257 0.000000000000396 ***
## robot_type1 1.4061 0.5514 2.550 0.0108 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.206
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
# wald CI
confint(model_glmer_humanform2,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -5.2704690 -3.028981
## robot_type1 0.3253775 2.486905
Creepy/cute
model_glmer_creepycute1 <- glmer(rank_glmer_creepycute ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# singularity warning, so use maximal complex random intercepts instead
model_glmer_creepycute2 <- glmer(rank_glmer_creepycute ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_creepycute3 <- glmer(rank_glmer_creepycute ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# allfit command
model_allfit_creepy1 <- allFit(model_glmer_creepycute1)
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00886124 (tol = 0.002, component 1)
## [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
### save summary in new object
summ_allfit_creepy1 <- summary(model_allfit_creepy1)
### look at fixed estimates
summ_allfit_creepy1$fixef # differences relatively small
## (Intercept) robot_type1
## bobyqa -3.080120 -0.9784994
## Nelder_Mead -3.082248 -0.9804868
## nlminbwrap -3.082237 -0.9804797
## nmkbw -3.082373 -0.9808375
## optimx.L-BFGS-B -3.082337 -0.9804823
## nloptwrap.NLOPT_LN_NELDERMEAD -3.082309 -0.9805309
## nloptwrap.NLOPT_LN_BOBYQA -3.084131 -0.9820012
### look at random effect
summ_allfit_creepy1$sdcor # differences small
## ParticipantID.(Intercept)
## bobyqa 0.3057479
## Nelder_Mead 0.3078533
## nlminbwrap 0.3078465
## nmkbw 0.3079814
## optimx.L-BFGS-B 0.3078599
## nloptwrap.NLOPT_LN_NELDERMEAD 0.3078471
## nloptwrap.NLOPT_LN_BOBYQA 0.3083834
## ParticipantID.robot_type1.(Intercept)
## bobyqa 0.5185656
## Nelder_Mead 0.5217025
## nlminbwrap 0.5216914
## nmkbw 0.5219071
## optimx.L-BFGS-B 0.5217053
## nloptwrap.NLOPT_LN_NELDERMEAD 0.5216964
## nloptwrap.NLOPT_LN_BOBYQA 0.5226008
## ParticipantID.robot_type1 robot.(Intercept)
## bobyqa 1.0000000 0.8075166
## Nelder_Mead 1.0000000 0.8075176
## nlminbwrap 1.0000000 0.8075198
## nmkbw 0.9999978 0.8074960
## optimx.L-BFGS-B 1.0000000 0.8074958
## nloptwrap.NLOPT_LN_NELDERMEAD 1.0000000 0.8074858
## nloptwrap.NLOPT_LN_BOBYQA 1.0000000 0.8090118
### check log likelihood
summ_allfit_creepy1$llik # differences small
## bobyqa Nelder_Mead
## -351.9023 -351.8998
## nlminbwrap nmkbw
## -351.8998 -351.8998
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## -351.8998 -351.8998
## nloptwrap.NLOPT_LN_BOBYQA
## -351.8998
### check whether all model ran
summ_allfit_creepy1$which.OK # all ran
## bobyqa Nelder_Mead
## TRUE TRUE
## nlminbwrap nmkbw
## TRUE TRUE
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## TRUE TRUE
## nloptwrap.NLOPT_LN_BOBYQA
## TRUE
# -> continue with singularity warning
summary(model_glmer_creepycute1) # significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## rank_glmer_creepycute ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 715.8 747.3 -351.9 703.8 1398
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6914 -0.2874 -0.2474 -0.1216 6.6311
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.09348 0.3057
## robot_type1 0.26891 0.5186 1.00
## robot (Intercept) 0.65209 0.8075
## Number of obs: 1404, groups: ParticipantID, 117; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.0801 0.3325 -9.263 < 0.0000000000000002 ***
## robot_type1 -0.9785 0.3255 -3.006 0.00265 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.413
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
# wald CI
confint(model_glmer_creepycute1,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -3.731853 -2.428399
## robot_type1 -1.616554 -0.340442
Intelligence
model_glmer_intelligence1 <- glmer(rank_glmer_intelligence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# singularity warning, so use maximal complex random intercepts instead
model_glmer_intelligence2 <- glmer(rank_glmer_intelligence ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_intelligence3 <- glmer(rank_glmer_intelligence ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# allfit command
model_allfit_int1 <- allFit(model_glmer_intelligence1)
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw :
## boundary (singular) fit: see help('isSingular')
## [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
# -> nmkbw doesnt issue warning
# use nmkbw
model_glmer_intelligence_nmkbw <- glmer(rank_glmer_intelligence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"), control = glmerControl(optimizer="nmkbw")) # no singularity warning
## boundary (singular) fit: see help('isSingular')
summary(model_glmer_intelligence_nmkbw) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## rank_glmer_intelligence ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
## Control: glmerControl(optimizer = "nmkbw")
##
## AIC BIC logLik deviance df.resid
## 620.0 651.4 -304.0 608.0 1374
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7797 -0.3294 -0.0154 -0.0120 6.3063
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 6.7120 2.5907
## robot_type1 7.2612 2.6947 -1.00
## robot (Intercept) 0.9014 0.9494
## Number of obs: 1380, groups: ParticipantID, 115; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.4928 0.8099 -6.782 0.0000000000119 ***
## robot_type1 3.4306 0.8062 4.255 0.0000208797161 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.867
## optimizer (nmkbw) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
# wald CI
confint(model_glmer_intelligence_nmkbw,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -7.080245 -3.905328
## robot_type1 1.850482 5.010740
Physical warmth
model_glmer_physicalwarm1 <- glmer(rank_glmer_physicalwarm ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# singularity warning, so use maximal complex random intercepts instead
model_glmer_physicalwarm2 <- glmer(rank_glmer_physicalwarm ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_physicalwarm3 <- glmer(rank_glmer_physicalwarm ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# allfit command
model_allfit_physwarm1 <- allFit(model_glmer_physicalwarm1)
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead : [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00366719 (tol = 0.002, component 1)
## [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
# -> neldermead doesnt issue warning
# use nelder mead
model_glmer_physicalwarm_nm <- glmer(rank_glmer_physicalwarm ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"), control = glmerControl(optimizer="Nelder_Mead")) # no singularity warning
summary(model_glmer_physicalwarm_nm) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## rank_glmer_physicalwarm ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
## Control: glmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 687.9 719.4 -337.9 675.9 1398
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8007 -0.2761 -0.1852 -0.1256 6.9623
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.05463 0.2337
## robot_type1 0.25603 0.5060 1.00
## robot (Intercept) 1.12285 1.0596
## Number of obs: 1404, groups: ParticipantID, 117; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1364 0.3711 -8.453 <0.0000000000000002 ***
## robot_type1 -0.6644 0.3630 -1.830 0.0672 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.183
# wald CI
confint(model_glmer_physicalwarm_nm,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -3.863653 -2.4091423
## robot_type1 -1.375884 0.0470625
Safety
model_glmer_safety1 <- glmer(rank_glmer_safety ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# singularity warning, so use maximal complex random intercepts instead
model_glmer_safety2 <- glmer(rank_glmer_safety ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_safety3 <- glmer(rank_glmer_safety ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# allfit command
model_allfit_safety1 <- allFit(model_glmer_safety1)
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw : [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
# -> nmkbw doesnt issue warning
# use nmkbw
model_glmer_safety_nmkbw <- glmer(rank_glmer_safety ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"), control = glmerControl(optimizer="nmkbw")) # no singularity warning
summary(model_glmer_safety_nmkbw) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: rank_glmer_safety ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
## Control: glmerControl(optimizer = "nmkbw")
##
## AIC BIC logLik deviance df.resid
## 798.1 829.5 -393.0 786.1 1398
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.4539 -0.3167 -0.2477 -0.2240 4.4636
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.003151 0.05613
## robot_type1 0.155162 0.39391 1.00
## robot (Intercept) 0.304425 0.55175
## Number of obs: 1404, groups: ParticipantID, 117; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.5951 0.2049 -12.666 <0.0000000000000002 ***
## robot_type1 -0.1513 0.1971 -0.768 0.443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.051
# wald CI
confint(model_glmer_safety_nmkbw,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -2.9966278 -2.1934831
## robot_type1 -0.5377004 0.2350084
Social competence
model_glmer_socialcomp1 <- glmer(rank_glmer_socialcompetence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
summary(model_glmer_socialcomp1) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: rank_glmer_socialcompetence ~ robot_type + (1 + robot_type |
## ParticipantID) + (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 710.2 741.7 -349.1 698.2 1398
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7180 -0.2705 -0.1914 -0.1545 6.1015
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.03131 0.1770
## robot_type1 0.20687 0.4548 -1.00
## robot (Intercept) 1.14659 1.0708
## Number of obs: 1404, groups: ParticipantID, 117; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.0188 0.3531 -8.549 <0.0000000000000002 ***
## robot_type1 0.3809 0.3491 1.091 0.275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.046
# wald CI
confint(model_glmer_socialcomp1,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -3.7108556 -2.326721
## robot_type1 -0.3033538 1.065085
Social intelligence
model_glmer_socialint1 <- glmer(rank_glmer_socialintelligence ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# singularity warning, so use maximal complex random intercepts instead
model_glmer_socialint2 <- glmer(rank_glmer_socialintelligence ~ robot_type + (1 | ParticipantID) + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# still warning, reduce
model_glmer_socialint3 <- glmer(rank_glmer_socialintelligence ~ robot_type + (1 | ParticipantID:robot_type) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
# allfit command
model_allfit_socialint1 <- allFit(model_glmer_socialint1)
## bobyqa :
## boundary (singular) fit: see help('isSingular')
## [OK]
## Nelder_Mead :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nlminbwrap :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nmkbw :
## boundary (singular) fit: see help('isSingular')
## [OK]
## optimx.L-BFGS-B :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD :
## boundary (singular) fit: see help('isSingular')
## [OK]
## nloptwrap.NLOPT_LN_BOBYQA :
## boundary (singular) fit: see help('isSingular')
## [OK]
### save summary in new object
summ_allfit_socialint1 <- summary(model_allfit_socialint1)
### look at fixed estimates
summ_allfit_socialint1$fixef # differences relatively small
## (Intercept) robot_type1
## bobyqa -3.231581 0.6340516
## Nelder_Mead -3.229998 0.6326828
## nlminbwrap -3.231586 0.6340509
## nmkbw -3.231526 0.6340493
## optimx.L-BFGS-B -3.231607 0.6340728
## nloptwrap.NLOPT_LN_NELDERMEAD -3.231703 0.6339966
## nloptwrap.NLOPT_LN_BOBYQA -3.009671 0.5034671
### look at random effect
summ_allfit_socialint1$sdcor # differences small
## ParticipantID.(Intercept)
## bobyqa 0.2414083
## Nelder_Mead 0.2399147
## nlminbwrap 0.2414050
## nmkbw 0.2413724
## optimx.L-BFGS-B 0.2414095
## nloptwrap.NLOPT_LN_NELDERMEAD 0.2413869
## nloptwrap.NLOPT_LN_BOBYQA 0.2095938
## ParticipantID.robot_type1.(Intercept)
## bobyqa 0.4917830
## Nelder_Mead 0.4891736
## nlminbwrap 0.4917778
## nmkbw 0.4916505
## optimx.L-BFGS-B 0.4917869
## nloptwrap.NLOPT_LN_NELDERMEAD 0.4917073
## nloptwrap.NLOPT_LN_BOBYQA 0.4384246
## ParticipantID.robot_type1 robot.(Intercept)
## bobyqa -1 1.223156
## Nelder_Mead -1 1.223106
## nlminbwrap -1 1.223156
## nmkbw -1 1.223266
## optimx.L-BFGS-B -1 1.223145
## nloptwrap.NLOPT_LN_NELDERMEAD -1 1.223160
## nloptwrap.NLOPT_LN_BOBYQA -1 1.239411
### check log likelihood
summ_allfit_socialint1$llik # differences small
## bobyqa Nelder_Mead
## -332.1377 -332.1399
## nlminbwrap nmkbw
## -332.1377 -332.1377
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## -332.1377 -332.1377
## nloptwrap.NLOPT_LN_BOBYQA
## -332.3146
### check whether all model ran
summ_allfit_socialint1$which.OK # all ran
## bobyqa Nelder_Mead
## TRUE TRUE
## nlminbwrap nmkbw
## TRUE TRUE
## optimx.L-BFGS-B nloptwrap.NLOPT_LN_NELDERMEAD
## TRUE TRUE
## nloptwrap.NLOPT_LN_BOBYQA
## TRUE
# continue with singularity warning
summary(model_glmer_socialint1) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: rank_glmer_socialintelligence ~ robot_type + (1 + robot_type |
## ParticipantID) + (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 676.3 707.7 -332.1 664.3 1386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7937 -0.2534 -0.1673 -0.1076 7.2344
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.05828 0.2414
## robot_type1 0.24185 0.4918 -1.00
## robot (Intercept) 1.49610 1.2232
## Number of obs: 1392, groups: ParticipantID, 116; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.2316 0.4228 -7.643 0.0000000000000212 ***
## robot_type1 0.6341 0.4124 1.537 0.124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.137
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
# wald CI
confint(model_glmer_socialint1,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -4.0602883 -2.402892
## robot_type1 -0.1742918 1.442415
Socialness
model_glmer_social1 <- glmer(rank_glmer_social ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
summary(model_glmer_social1) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: rank_glmer_social ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 707.3 738.8 -347.7 695.3 1398
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6848 -0.3341 -0.1905 -0.0887 8.0403
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.0236 0.1536
## robot_type1 0.3206 0.5662 -1.00
## robot (Intercept) 2.0908 1.4460
## Number of obs: 1404, groups: ParticipantID, 117; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.3430 0.4881 -6.849 0.00000000000745 ***
## robot_type1 0.7492 0.4771 1.570 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 -0.177
# wald CI
confint(model_glmer_social1,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -4.2997345 -2.386345
## robot_type1 -0.1858341 1.684210
Social warmth
model_glmer_socialwarm1 <- glmer(rank_glmer_socialwarmth ~ robot_type + (1 + robot_type| ParticipantID) + (1| robot), data = df_survey, family=binomial("logit"))
## boundary (singular) fit: see help('isSingular')
summary(model_glmer_socialwarm1) # not significant
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## rank_glmer_socialwarmth ~ robot_type + (1 + robot_type | ParticipantID) +
## (1 | robot)
## Data: df_survey
##
## AIC BIC logLik deviance df.resid
## 729.0 760.5 -358.5 717.0 1386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7245 -0.2518 -0.2150 -0.1761 6.5930
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantID (Intercept) 0.009416 0.09703
## robot_type1 0.240270 0.49017 1.00
## robot (Intercept) 0.917681 0.95796
## Number of obs: 1392, groups: ParticipantID, 116; robot, 12
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.8990 0.3157 -9.183 <0.0000000000000002 ***
## robot_type1 -0.2713 0.3097 -0.876 0.381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## robot_type1 0.057
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
# wald CI
confint(model_glmer_socialwarm1,parm="beta_",method="Wald")
## 2.5 % 97.5 %
## (Intercept) -3.5177693 -2.2802313
## robot_type1 -0.8782597 0.3356416
Social competence